Cleanlab/token-classification-tutorial

该数据集包含了在cleanlab教程中用于标记分类任务的预测概率。它展示了如何使用cleanlab来识别和修正标记分类数据集中的标签问题，例如命名实体识别（NER）任务，其中序列中的每个标记都被分配了一个类别标签。数据集主要用于教育目的，演示cleanlab在检测标记分类数据集中的问题（如错误标记的标记、不一致的实体边界注释、错误标记的实体类型等）方面的能力。数据集包含一个pred_probs.npz文件，这是一个NumPy压缩存档，包含每个标记的模型预测类别概率分布。

This dataset contains predicted probabilities for token classification used in the cleanlab tutorial: [Token Classification](https://docs.cleanlab.ai/stable/tutorials/token_classification.html). The dataset demonstrates how to use cleanlab to identify and correct label issues in token classification datasets, such as Named Entity Recognition (NER) tasks where each token in a sequence is assigned a class label. The dataset was created for educational purposes to demonstrate cleanlabs capabilities for detecting issues in token classification datasets, such as incorrectly labeled tokens, inconsistent entity boundary annotations, mislabeled entity types, etc. It includes a pred_probs.npz file, which is a NumPy compressed archive containing model-predicted class probabilities for each token.